Systems and methods for determining a significance index

ABSTRACT

Systems, methods, and computer-readable instructions are configured to calculate an aggregated significance score for consumer goods or website interactions by aggregating historical significance scores and calculating a reference significance score for a benchmark. A relative significance score is derived for each of the consumer goods or website interactions based upon the significance score for each of the consumer goods or website interactions and the relative significance score. A consumer priority index is derived indexed to a relevant measure based on the aggregated significance scores and the reference significance score.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/736,756 filed Jan. 7, 2020, which is a continuation of U.S. patentapplication Ser. No. 14/723,165 filed May 27, 2015, which is acontinuation-in-part of PCT application PCT/US2013/056033 filed Aug. 21,2013, which claims the benefit of U.S. patent application Ser. No.13/687,927 filed Nov. 28, 2012 (now U.S. Pat. No. 8,478,676). The entiredisclosures of the applications referenced above are incorporated byreference.

BACKGROUND

Consumer confidence and sentiment indices are valuable indicators thatbusiness practitioners, policy makers, investors, and traders use toinform their decisions. Currently most indices are generated viasurveys, text and data mining, as well as a number of random samplingmethods. These indices are too onerous to administer. Most importantlythey are inaccurate as people do not always answer survey questions inways that are consistent with their behavior, and the random sampleschosen are not statistically representative due to the heterogeneousfragmentation of the population. For an index to be truly representativeof its claims, it should capture granular explicit behavior from a verylarge number of transactions of a very large homogeneous populationunder controlled conditions.

SUMMARY

The present disclosure describes an index that explicitly reflects thesentiment and confidence of traders, whereby each of a plurality oftransactions is captured, assigned a sentiment score, and can beaggregated using a number of algorithms, and statistical methods. Inparticular, the present disclosure relates to methods and systems fordetermining a quantitative sentiment index based upon securitiescontained within a number of brokerage accounts.

In general, one aspect of the subject matter described in thisspecification can be embodied in systems for calculating a volatilityscore for each of a plurality of securities and calculating a referencevolatility score for a benchmark. A relative volatility score is derivedfor each of the plurality of securities based upon the volatility scorefor each of the plurality of securities and the reference volatilityscore. An aggregated volatility score is derived for each of a pluralityof accounts based in part on securities held within an account and therelative volatility scores for the securities. A sentiment index isdetermined based upon the aggregated volatility score for each of theplurality of accounts. Other implementations of this aspect includecorresponding systems, apparatuses, and computer-readable mediaconfigured to perform the actions of the method.

In one embodiment, a system is described to calculate a significanceindex comprising one or more electronic processors, operably connectedto a memory. The one or more electronic processors are configured toreceive financial instrument transaction information regarding exchangeof a first financial instrument for a second financial instrument,derive a significance score for the second financial instrument, andgenerate a significance index based upon the significance score for thesecond financial instrument and a plurality of additional significancescores.

In another embodiment, a method is described for managing a userexperience based upon a consumer index. The method may comprisereceiving financial instrument transaction information associated witheach of a plurality of accounts regarding exchange of a first financialinstrument for a second financial instrument. The method may alsoinclude determining a significance score associated with the financialinstrument transaction for each account of the plurality of accounts,calculating a significance index for the second financial instrument byaggregating the significance score for each of the plurality ofaccounts, determining a consumer priority index indexed to a relevantmeasure based on the aggregated significance score for each of theplurality of accounts, and modifying a sales interface associated withthe second financial instrument based upon the consumer priority index.

In another embodiment a system is described comprising one or moreelectronic processors, operably connected to a memory. The one or moreelectronic processors are configured to receive security transactioninformation associated with each of a plurality of accounts regardingexchange of a financial instrument for a security, determine asignificance score associated with the security transaction for eachaccount of the plurality of accounts, and calculate a significance indexfor the security by aggregating the significance score for each of theplurality of accounts.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects,implementations, and features described above, further aspects,implementations, and features will become apparent by reference to thefollowing drawings and the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe disclosure will become more apparent and better understood byreferring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a block diagram of a system for providing a user with abrokerage firm's online services over the Internet.

FIG. 2 is a block diagram of a system for calculating and providing aquantitative retail sentiment index based on client behavior inaccordance with an illustrative embodiment.

FIG. 3 is a flow diagram of a method for determining a quantitativeretail sentiment index based on client behavior in accordance with anillustrative embodiment.

FIG. 4 is a graph over a period of time of a quantitative retailsentiment index and a benchmark in accordance with an illustrativeembodiment.

FIG. 5 is a graph over a period of time of a quantitative retailsentiment index and a benchmark in accordance with an illustrativeembodiment.

FIG. 6 is a graph over a period of time illustrating the number ofaccounts within a range of a quantitative retail sentiment index inaccordance with an illustrative embodiment.

FIG. 7 is a graph over a period of time illustrating a benchmark and aquantitative retail sentiment index in accordance with an illustrativeembodiment.

FIG. 8 is a graph of a two axis index in accordance with an illustrativeembodiment.

FIG. 9 is a block diagram of a computing device.

FIG. 10 is a graph over a period of time of a quantitative retailsentiment index of a sample investor portfolio compared to the portfoliovalue in accordance with an illustrative embodiment.

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and made part of this disclosure.

DETAILED DESCRIPTION

In reference to FIG. 1 , a system for providing online brokerageservices to users over the Internet is shown. Local machines 102 a-c mayaccess a server 104 hosting the online services provided by a brokeragefirm. A local machine can be a desktop computer, a laptop, a tabletcomputer, a smart phone, or any other computing device. The server 104may be a number of servers or a server farm. The brokerage firm'sservices may utilize real-time trading data to provide a user with themost up to date and current trade information. The server 104 can alsoallow users of local machines 102 a-c to create accounts and tradevarious securities via various exchanges 106 a-b.

As users trade securities via the server 104, the server 104 can keepdetailed records describing the various trades and holdings of users.These records can be used to provide the user with information regardingtheir previous trades and their current holdings. For example, a usercan access a history of all previous trades, which can provide the typeof trade, the date of the trade, a security identifier, a unit price, anumber of units purchased, and a total trade amount. Other informationcan also be kept as part of the record keeping process, for example, butnot limited to, a user identifier, a time the trade was initiated, atime the trade was executed, etc. In addition, the server 104 can alsoanalyze the trades across one or more users or accounts.

In one implementation, a sentiment index can be calculated from actualtrades of a large number of clients, making the sentiment index aquantitative index. The sentiment index can be used to determine the“sentiment” of clients, portfolios or accounts. The sentiment may begenerally described as an overall attitude about equity markets based onholdings and arrangement of holdings of a client, portfolio, or account.In one embodiment, the sentiment is reflected in the action and/orinaction taken with respect to an asset, typically a security. In oneimplementation, the sentiment is reflected in the risk taken by theaccount. The risk may be relative to a benchmark security or index, suchas, but not limited to, the S&P 500®. The sentiment may reflect ahistorical covariance between the security and the benchmark, such ascorrelated volatility. For example, beta (β) may be used as anexpression of sentiment by comparing the correlated volatility of asecurity to the benchmark.

In various implementations, the sentiment index can be calculated at aclient level, a portfolio level, or an account level. The portfolio andaccount may belong to a single client and reflect the sentiment of asingle person. Alternatively, a portfolio or account may belong tomultiple individuals and reflect the sentiment of multiple people. Aportfolio or account may belong to a business entity or financialadvisor and reflect the sentiment of the manager of that portfolio oraccount in their business capacity. Each portfolio or account may beassociated with one or more brokerage service entities. Accordingly, inthe examples described below, clients or portfolios should be understoodas being applicable in place of accounts unless otherwise noted. In someimplementations, the clients, portfolios, or accounts that are used incalculating the sentiment index can be homogenous. For example, theclients can all be classified as investors, individual traders,shoppers, etc.

In some implementations, the sentiment index can be thought of asdescribing an account as either a risk-on account or a risk-off account.Non-limiting examples of risk-on positions include long equity positionsand long leveraged equity exchange-traded funds (ETFs). Non-limitingexamples of risk-off positions include long inverse equity ETFs, bonds,bond funds, and cash balances. Some or all of the positions within anaccount can be aggregated to determine if the account is a risk-onaccount or a risk-off account, and how much risk the account is exposedto compared to the benchmark.

In addition, the number of clients, portfolios or accounts used incalculating the sentiment index can vary depending on the type of indexbeing calculated. For example, sentiment indices can be calculated usingaccounts based upon segmentation or account types, including whether theaccount is a financial advisor account, an individual account, acustodian account, etc. Because each account is scored, possiblesegmentations include vintage, trade segments, generation, businessunits, asset size, etc. In one embodiment, the sentiment index iscalculated using a large number of accounts. The number of accounts usedis large enough that individual accounts cannot be identified from thecalculated index. In one implementation, each account has the sameweight in calculating the index, regardless of the monetary amount ofthe account. In such an embodiment, the sentiment index reflects thesentiment of the aggregate account holders rather than the aggregatemoney, thus the sentiment of accounts with less value is not masked bythe sentiment of accounts with large monetary value. In otherimplementations, accounts are weighted based upon a factor such as themonetary value of the account, number of trades, volume of trades, etc.

FIG. 2 is a block diagram of an implementation of a system forcalculating and providing a quantitative retail sentiment index based onaccount behavior in accordance with an illustrative embodiment. A backoffice data store 202 contains trading records of accounts associatedwith one or more brokerage services. As described above, these tradingrecords contain information about individual security trades from alarge number of accounts. The back office data store 202 can alsoinclude information about the accounts themselves, e.g., aggregated datafrom the trading records, client demographic data, etc. A market datastore 204 contains historical prices of various securities that can betraded. An enterprise data store 206 can be used to store various datafor use in calculating one or more sentiment indices as well as storingthe indices themselves. Two or more of the various data stores 202, 204,and 206 can be located on a single server. The enterprise data store 206can store a volatility score for each security. As described in greaterdetail below, these volatility scores can be used to calculate avolatility score for each account, portfolio, client, etc. Thesevolatility scores can also be stored on the enterprise data store 206.The volatility scores for the accounts, portfolios, and/or clients, canthen be aggregated to create various sentiment indices, which can bestored on the enterprise data store 206. In other implementations, thesentiment indices can be stored in other various data stores that areseparate from the enterprise data store 206.

A query engine 208 can be used to access the stored sentiment indiceslocated in the enterprise data store 206 or other data store. The queryengine 208 provides an interface for various applications to access andincorporate one or more of the sentiment indices stored in theenterprise data store 206. For example, client facing applications 210,such as a market overview page, can incorporate a sentiment index. Inanother example, the query engine 208 can be used to access an account'sindex score for display to the client who owns the account. Non-clientfacing applications 212 can also access the various sentiment indices.As a non-limiting example, an advertising or marketing selection processcan use the scores associated with accounts to determine which accountsto send marketing materials to as well as to tailor the marketingmaterial or advertisement to the account. Other applications, such asreporting, analysis, and decision support 214, can also access andutilize the data from the enterprise data store 206.

As mentioned above, the back office data store 202 and the market datastore 204 contain data that can be used to calculate various sentimentindices. FIG. 3 is a flow diagram of a method 300 for determining aquantitative retail sentiment index based on account behavior inaccordance with an illustrative embodiment. Additional, fewer, ordifferent operations of the method 300 may be performed, depending onthe particular embodiment. The method 300 can be implemented on acomputing device. In one implementation, the method 300 is encoded on acomputer-readable medium that contains instructions that, when executedby a computing device, cause the computing device to perform operationsof the method 300.

In one implementation, before calculating a sentiment index, someinitial data is needed. The risk associated with each security must beknown. For example, sufficient historical prices for each securityincluded in the sentiment index and the benchmark to provide astatistically significant correlated volatility determination areneeded. In one embodiment, the benchmark used to derive a relativevolatility can be selected from the historical prices of the securities.A volatility for each of the securities can then be calculated using thehistorical prices (302). In one implementation, the volatility iscalculated using a rolling exponential average volatility over a timeperiod. In another implementation, a rolling average is subtracted fromthe calculated volatility. This removes the securityappreciation/depreciation trend from the security's volatility. How thevolatility score is calculated can depend upon the type of security. Forexample, in one implementation, as described in greater detail below,the volatility score of a stock is calculated differently than theassigned volatility score of an option. Other securities can have theirvolatility score calculated in the same way. For example, in oneimplementation, volatility scores for stocks and warrants can becalculated in the same manner. Using an exponential average volatilityweights current events higher than older events. This helps to preventscores from changing significantly when older events are no longer partof the time period. As a non-limiting detailed example, the volatilityis calculated as the exponential average change in the value of asecurity over a year. That is, how much did the security change in valuethrough the year. The average change in the security value can becalculated as, but not limited to, daily change, weekly change,quarterly change, etc. Other volatility values can also be used. Forexample, beta, correlated beta, total beta, etc., can be used. Inaddition, a volatility value can be used without weighting, withweighting, or exponentially weighted.

For newly listed securities, the volatility score can be set to aninitial amount for a period of time. For example, for the first fourweeks that a security is listed, the security's volatility score can beset to 1, regardless of the price history of the security. After theperiod of time has expired, the volatility value can be calculated asdescribed above. For some newly listed securities, such as, ETFs, aninitial value is not required as the volatility score can be calculatedfor the ETF using the historical prices of the securities that make upthe ETF.

The volatility for a benchmark is also calculated (304). The volatilityfor the benchmark is calculated in the same manner as other securities.The benchmark can be an index such as the S&P 500® or other indices thattrack stock markets. The benchmark is used to calculate a relativevolatility score for the other securities (306). In one implementation,the relative volatility for a security is calculated as the volatilityscore of the security divided by the volatility score of the benchmark.

In some implementations, a sentiment index is based upon scores thattake into account liquidation values of portfolios with the value of theaccount's positions. In these implementations, options data can bederived (308). For example, this data can include delta values, thesecurity identifier, underlying price, underlying relative adjustedvolatility score, and an option multiplier. Delta values for options arebased upon the option price movement relative to the underlyingsecurity. As described in greater detail below, the delta values and theunderlying security values can be used in calculating the volatilityscore for an account, portfolio, or client.

When the chosen benchmark is a stock market index, such as the S&P 500®,securities with a high positive volatility score are considered bullishsecurities. That is, the more volatile the security, the more bullishthe security. Some securities will have a calculated volatility scorethat is positive, but should be negative based upon the chosenbenchmark. These securities are those that are assumed to be heldbecause the security moves opposite of the chosen benchmark. Forexample, some securities are negatively correlated to the benchmark,such as, but not limited to, some ETFs. Securities can have theirvolatility score adjusted in various ways (310). For example, avolatility score can be scaled based upon how often the security movesin the same direction as the benchmark. As another example, thevolatility score can be reversed to be a negative value. In oneimplementation, these securities can be identified by determining asecurity's coherence with the benchmark. Securities can have theirrelative volatility scores changed to a negative value based upon anegative correlation threshold of 25% to 60% with the benchmark. Inanother implementation, the relative volatility score can be changedbased on the strategy or structure of the security. For example, inverseETFs, long term bonds, etc., can have their relative volatility scorereversed.

Options can also have an assigned relative volatility score based uponthe underlying security of the options. In one implementation theassigned relative volatility score can be changed to a negative valuebased upon the option's delta values and the underlying security. Thiscan occur in a similar manner to warrants, bonds, ETFs, etc., asdescribed above. In addition, the relative volatility score can have aninitial negative value based on the type of option, e.g., a put of asecurity. An initial negative value, however, can be changed to apositive value based upon the negative correlation threshold asdescribed above.

In one implementation, holding stocks, unlike bonds, ETFs, etc., isalways considered bullish, regardless of the correlation with thebenchmark, and therefore, will have a positive relative volatilityscore. This is true even when a stock is purchased that has gone downwhen the benchmark has gone up. Conversely, shorting stocks will beconsidered bearish and have a negative relative volatility score.

In another implementation, the volatility score for a security can becalculated using a trust factor. The trust factor is a value thatindicates how a security moves in relation to a benchmark on a givenday. For example, the trust factor for a security is how likely thesecurity moves in the same direction as the benchmark on a given day.Accordingly, the trust factor attempts to capture securities that werepurchased with the intent of hedging or betting against the benchmark.In one implementation, the trust factor is weighted towards a securitymoving in the direction of the benchmark. This requires a strongnegative correlation between a security and the benchmark prior tomaking the trust factor a negative value. In one implementation, thetrust factor for a security over time is a smooth curve, such that thetransition from a positive to negative, or vice versa, trust score issmooth.

As an example of calculating a trust factor, the trust factor can be avalue between −1 and 1. If a security moves in the same direction as thebenchmark some threshold amount, the trust factor can be set to 0.99.The threshold amount can be a value such as, but not limited to, 10%,20%, 21%, 25%, etc. If the security moves in the opposite direction asthe benchmark some threshold, such as, but not limited to, 75%, 79%,80%, 85%, etc., the trust factor can be −0.99. For securities that fallbetween the two thresholds, the trust score can be determined on alinear basis or on an exponential basis. For example, a linear equationcan be used that weights the trust factor to be positive when thesecurity moves in the same direction as the security some amount, suchas, but not limited to, 55%, 60%, 66%, 70%, etc.

The trust factor can be used to modify the relative volatility score ofa security. In one implementation, the relative volatility score ismultiplied by the trust factor to generate an updated relativevolatility score. Because the trust factor takes into account how thesecurity moves compared to the benchmark, the trust factor can be usedto change the sign of the relative volatility score. The trust factordoes not need to be applied to every security. For example, the trustfactor can be applied to all securities with the exception of individualstocks. Not applying the trust factor to stocks assumes that clients arerational and a stock purchase is made with the assumption that the stockprice will increase over time, regardless of how the stock has performedin the past.

In another implementation, a relative volatility score and/or aquantitative retail sentiment index can be graphed. FIG. 8 is a graph ofa two axis index in accordance with an illustrative embodiment. Thex-axis shows the volatility which can be based on the relativevolatility or the absolute volatility of security. The y-axis maps thedirection of the security in comparison with a benchmark. The y-valuecan be based on probability or correlation of the security with thebenchmark. The two data points are plotted on the map and the valuebecomes the relative volatility score and/or quantitative retailsentiment index. Example index 802 and 804 correspond to the index valuefor a security on two separate dates, e.g., Sep. 30, 2012 and Oct. 31,2012 respectively.

In some implementations, newly listed securities or outlier securitiescan be removed from further analysis or place holder values can be usedfor these securities. If these securities are filtered from furtheranalysis, the securities will be ignored in any accounts that havepositions in these securities. Thus, the filtered securities will notaffect the derived sentiment index. Other securities can be filtered,such as, but not limited to, pink sheets, bulletin board securities,non-standard assets, etc.

After the relative volatility score is calculated for each security, anaggregated volatility score for an account is derived (312). In oneimplementation, the aggregated volatility score is calculated on aweighted basis based upon the value of each security relative to thetotal account value. In other implementations, an aggregated volatilityscore can be calculated for each client or each portfolio. An indexbased upon the aggregated volatility scores for each account can then bedetermined (314). In one implementation, each account has one vote inthe index. That is, each account has the same relative impact on theindex as any other account, regardless of the monetary value of theaccount. In other implementations, the index can be weighted based uponthe monetary value of an account, the number of trades, the volume oftrades, etc. Regardless of any weighting, the index is calculated basedupon actual account behavior such as trades and holdings of securities.Before calculating the index, certain accounts can be filtered. Forexample, accounts that do not have any positions in any securities canbe removed, i.e., accounts that have a zero balance or hold only cashpositions. After these accounts are filtered, the index can becalculated based upon the aggregated volatility score of the remainingaccounts. In addition, various indices can be created based upon asubset of account data. For example, a total sentiment index can becalculated from all of the account data. As another non-limitingexample, sentiment indices based upon demographic data, position in acommon security, monetary account value, etc., can be calculated. Inanother implementation, a sentiment index can be calculated using onlysecurities from a particular market.

In another implementation, the volatility score for a security canchange for an account based upon how the client holds the security. Forexample, holding options in a security or buying securities on margincan change the volatility score compared to holding the securityoutright. In this implementation, the volatility score for each securityheld in an account is calculated based upon the security's relativevolatility score. In one implementation, the account specific relativevolatility score for a position in a security is calculated as: thesecurity quantity x the delta value x quantity multiplier x underlyingsecurity price x relative volatility score of security. In oneimplementation, the delta value can be a proxy of an option expiring inthe money. The delta value can also be based the Black-Scholes model orcalculated as the percentage movement in the price of an option dividedby the percentage movement in the underlying security. The quantitymultiplier is the number of shares of the underlying security of theoptions contract. The security quantity is the number of securities,e.g., option contracts that are held. The account-specific relativevolatility score can then be summed across an account to derive a totalaccount-specific relative volatility score. The total account-specificrelative volatility score is used to generate a relative volatilityscore for the account by dividing the total account specific relativevolatility score by the liquidation value of the account. The relativevolatility score for the accounts can then be used to calculate varioussentiment indices as described above.

In one embodiment, a synthetic beta is calculated for an account as theaccount's volatility score. The synthetic beta is a weighted score thatfactors in leverage (e.g., margin purchases) and hedging, such asthrough the use of derivatives (e.g., options). In one implementation,margin purchases are taken into account. The synthetic beta is equal tothe security's beta multiplied by the security's value divided by itsliquidation value (i.e., net the margin loan). In a furtherimplementation, margin purchases may be accounted for on a client,account, or portfolio basis rather than at the individual securitylevel. For example, the volatility score for the account is calculatedand then modified by the account's holdings divided by liquidationvalue.

As a non-limiting example of the above account-specific relativevolatility, a first account has a $10,000 liquidation value holding$10,000 worth of an S&P 500® ETF. In this example, the S&P 500® index isused as the benchmark. The account specific relative volatility scoreis 1. A second account having a $10,000 liquidation value but holding$20,000 worth of the S&P 500® index would have an account specificrelative volatility score of 2. In the second account, a portion of theS&P 500® index position is purchased on margin, which accounts for thedifference between the liquidation value and the value of the holdings.

FIGS. 4 and 5 are graphs over a period of time of a quantitative retailsentiment index and a benchmark in accordance with an illustrativeembodiment. In these figures, the quantitative retail sentiment index istermed a synthetic beta and is graphed against the S&P 500® index (SPX).In FIG. 4 , accounts that are cash only and accounts that hold positionsin one or more securities are used to calculate the sentiment index. Incontrast, FIG. 5 shows the sentiment index calculated using onlyaccounts that contain at least one position in a security. As shown, thesentiment index on average is higher, since all cash holdings have lowvolatility and thus a low or zero relative volatility score. Thesentiment index is shown on a monthly basis, but could be shown at adifferent frequency, e.g., weekly, daily, hourly, etc.

FIG. 6 is a graph over a period of time illustrating the number ofaccounts within a range of a quantitative retail sentiment index inaccordance with an illustrative embodiment. This graph illustrates thepercentage of accounts that have a particular range of aggregatedvolatility score. As shown in FIG. 6 , the percentage of accounts thathave a less than 0 aggregated volatility score fluctuates. An aggregatedscore less than 0 indicates that the account moves in the oppositedirection of the benchmark.

FIG. 7 is a graph 700 over a period of time illustrating a benchmark anda quantitative retail sentiment index in accordance with an illustrativeembodiment. A line 704 indicates an average, such as a daily average, ofa benchmark, such as S&P 500®. In addition, the graph 700 can include aline 702 indicating the ending value of the benchmark. A line 706indicates the performance of a quantitative retail sentiment index. FIG.7 allows for a visual comparison of the benchmark to the quantitativeretail sentiment index.

As described above, once the aggregated volatility scores have beencalculated for accounts, portfolios, and/or clients, these scores can beused in various ways. As one non-limiting example, the aggregatedvolatility scores can be used to send targeted marketing materials oradvertisements to clients. The aggregated volatility scores can also beused to enhance data provided to a client. For example, the aggregatedvolatility score can be presented to the client, along with anexplanation of the score and its meaning. In addition, particular typesof securities can be identified based upon the aggregated volatilityscores. For example, the securities that are currently being traded inaccounts associated with a range of aggregated volatility scores can bedetermined. These can then be provided to the client; for example, thesecurities that are currently being bought by bullish clients or sold bybearish clients. As another example, securities that were bought or soldby accounts whose aggregated volatility score changed more than apredetermined amount over a period of time can be determined andprovided to clients.

In addition to the use of the aggregated volatility scores, the relativevolatility scores of securities can be used in various ways. As oneexample, a client can search for securities based upon their relativevolatility scores. In one implementation, a client can search forsecurities that are related to their relative volatility score. Forexample, a client may want a portfolio to have a target relativevolatility score that is different than the portfolio's current relativevolatility score. Securities can be searched that if held in a certainmanner would move the current volatility score toward the targetrelative volatility score. The search results can also indicate as tohow the security should be held, e.g., long, short, etc., to have theintended change to the portfolio's relative volatility score.

In another implementation, a significance score may be related to afinancial instrument transaction. In this context, a financialinstrument is a tradable asset of any kind; either cash, evidence of anownership interest in an entity, or a contractual right to receive ordeliver cash or another financial instrument, asset, such as a consumergood, or service. The financial instrument transaction may include theexchange of one financial instrument, such as cash or the like for asecond financial instrument, for example assets such as individualstocks, stock indexes, custom lists of stocks, real estate, orcollectibles such as coins, comic books, toys, collectible. Eachtransaction or a piece of transaction is indexed to a measure that isrelevant/significant to the person/account involved in the transaction.Delta and beta adjusting of the index is possible so that derivative andmore or less volatile transactions can be more accurately compared toeach other.

In one implementation, information regarding a financial transaction isreceived. The significance may be determined with respect to a portionof the transaction compared to the whole or of the transaction comparedto a property of the associated account. An example of the former, inthe context of a consumer goods transaction, is the total value of goodof interest in comparison to the total transaction. As an example of thelatter, the total % the transaction represents an account's securitiesportfolio or the accounts available purchasing power. Adjustments of thesignificance may be made due to an amount of activity taking place inthe portfolio compared to a baseline of that account's activities.

In the context of transactions involving securities, once thesignificance or confidence levels have been measured for the majority ofsecurities, they can be aggregated (weighted average, etc.) into customrepresentations at an individual account or portfolio level. Investorscan get an indication of how confident or how bullish or bearish otherinvestors, such as the entire universe included in the significanceindex or only that of “active” investors or investors of a certainvolume, certification, or net worth, are on the investor's owncustomized portfolio. One way to look at an investor's portfolio is tochart the confidence over time compared to the investor's individualportfolio performance. A simulation engine can be plugged into this viewso investors could see how particular changes in their portfolio couldchange how confident active investors are on their portfolio. If theindividual investor would like to take action they can manually trade ora rebalancing engine can automatically route the orders.

FIG. 10 is a graph over a period of time of a quantitative retailsentiment index of a sample investor portfolio compared to the portfoliovalue according to an illustrated embodiment. In this example portfolio,there are four stocks shown, AAPL, IBM, GE, BAC and a cash holding. Themedian of confidence levels of the stocks are shown graphed over time.In comparison, the value of the portfolio is shown graphed over the sametime interval. In FIG. 10 , positions that are cash only and thepositions in individual stocks are used to calculate the sentiment indexAs shown, the cash holdings have low volatility and are calculated with1.0 score. Also as shown, the median investor confidence reading is 1.2indicating a bullish position. The sentiment index could be shown at adifferent frequency, e.g., weekly, daily, hourly, etc.

In one implementation, the significance score or confidence level of aparticular stock may be displayed. In addition to showing an aggregationsuch as average or median of a portfolio of stocks, a distribution ofconfidence level of the individual stocks in the portfolio may bedisplayed in order to visualize the skew in confidence. Such skews inconfidence may be visualized and displayed over time.

In the context of transactions involving consumer goods, thesignificance determination can be used to correlate with the consumer'spriorities by calculating a priority score. The priority scores may beaggregated to form a consumer priority index. For example, a store maylook at the purchase of a certain good relative to all purchases made ata certain point in time to gauge how much of a priority that certaingood had at that point. If sales of the certain good have a much higherpriority for people at a certain point in time, the store can use theinformation in various ways. The store may want to shuffle end caps oralter their displays in other ways to increase sales. The store may beable to organize inventory for optimization. The store may be able toalter upselling strategies.

In another embodiment in the context of securities transactions, thesystem can be used to look at market activity each day to give insightinto how significant each symbol traded is based on how confidentinvestors were at the time of each trade. This provides a differentperspective than looking at volume or dollars traded. The significanceof dollar amount traded are different depending on investor portfolios(e.g. a $1 k gain or loss in a $10 k portfolio vs. a $10 M portfolio).Thus, significance can be calculated by determining the amount thetransaction compares to 1) the entire account value, 2) the total valueof transactions over a certain time period (e.g., last 7 days), 3) thetotal cash purchasing power (rather than the value of the account, whichmay reflect decisions made long ago), and 4) the purchasing power of theaccount including margin. Adjustments of the significance may be madethrough beta and delta adjusting, incremental expectations due tobaselining significance of an individual client, and leveraging fromoptions. The significance may also be adjusted up or down based onfactors associated with the account and/or the good/security receivedand/or the financial instrument used (cash, margin, etc). For example, aconsideration factor may be applied based upon the type of the firstfinancial instrument. The significance may be increased where afinancial transaction utilizes credit or margin purchasing power, as anindication of the willingness of the account user to leverage themselvesto complete the transaction.

In one embodiment, the financial instrument transaction is analyzed todetermine if it is a hedging event. For example, the financialinstrument transaction may be compared to recent transactions todetermine if there is an offsetting transaction or a related transactionsuch as a derivative purchased. A hedging factor may be applied toreduce the significance score of the associated asset. This may also beadjusted for frequency of behaviors such as the frequency of sellersbeing risk on versus sellers that are risk off. Frequency of behaviorscould also be used to weight the significance score. There may also bebeta or delta adjustment to render the significance score relevantregardless of hedging.

In another embodiment, the disclosure describes a system or method tocalculate a consumer priority index for any financial instrumenttransaction, including securities transactions or purchased retail orwholesale consumer goods. The system or method calculates a significancescore associated with a particular asset received by the account such asa security or consumer good. The significance scores for a plurality ofaccounts are aggregated to determine a significance index for the givenfinancial instrument transaction indexed to a relevant measure. Relevantmeasures may be temporal, such as time of day, day of the week, day ofthe month, month of the year. Relevant measures may be security type,security name, aisle number or physical store location, expiration dateof inventory, color, style, price, or any other relevant measure thatcan be tied to the consumer goods. The significance index may becalculated as a priority index to determine which of a plurality ofassets acquired in a financial instrument transactions was the mostimportant to the consumer based upon, for example, the relative value ofthe acquired items to the total financial instrument transaction value.

In addition, the system can indicate the scale of disagreement betweenbuyers of the stock and sellers. For example, a stock's value may godown due to a larger volume of sells than buys, but the aggregatesignificance may indicate that the buyers' transactions have moresignificance to them than the sellers. A possible scenario would bewhere a large institutional investor sells on a light volume day thatotherwise is primarily buys.

In another embodiment, the system can be used to look at market activityfor an initial public offering (IPO) and compare the confidence ofclients in their buys of a certain IPO relative to other IPOs.

In another embodiment, the system and methods determine a baselinesignificance for a class or category of items, such as S&P 500 ® stocksor goods sold at Target®. This baseline can be indexed and compared tothe significance index for a given individual item within the class orcategory, such as for an individual stock or good sold.

In another embodiment, the system and methods store historicalsignificance data to provide a timeline to the index to providesignificance changes over time.

In another embodiment, the disclosure describes a system or method tomodify a layout for consumer interaction: for example, by calculating aconsumer priority index for interactions with a website. The system ormethod uses a historical record of significance scores associated withan account to modify the content and/or layout of a website. As afurther example, a layout for a consumer establishment such as a storemay be modified based on the significance index associated with itscustomers. The significance index may indicate that alcohol is apriority amongst their purchases on Friday evenings; thus the storechanges its layout to more prominently display alcohol during the timeperiods associated with a high significance metric.

Each interaction with the website is indexed to a measure that isrelevant to the user. The more significant the interaction with thewebsite, the more relevant the factor may be for optimization orstreamlining of the user/website interactions. For example, a web sitethat allows for the trading of securities could optimize and streamlinethe trading path for each client based on the point in time where theclient is making their most significant trades. In addition, the storedsignificance scores related to website interactions could be used todetect anomalies in behavior. For example, a web site that allows forthe trading of securities could detect when clients are makingabnormally large or small trades, purchases, or transactions relative totheir account size, previous behavior as related to the storedsignificance scores. This may also aid in fraud detection. Continuingthe example of a web site that allows for the trading of securities,information can be tracked across portfolios or across the total clientbase to understand when active investors/clients are making small orlarge portfolio adjustments. Being able to index significant moves inportfolios to certain measures, such as time of day or day of the week,may be able to allow investors to potentially deploy trading strategies.

FIG. 9 is a block diagram of a computer system in accordance with anillustrative implementation. The computing system 900 includes a bus 905or other communication component for communicating information and aprocessor 910 or processing circuit coupled to the bus 905 forprocessing information. The computing system 900 can also include one ormore processors 910 or processing circuits coupled to the bus forprocessing information. The computing system 900 also includes mainmemory 915, such as a random access memory (RAM) or other dynamicstorage device, coupled to the bus 905 for storing information, andinstructions to be executed by the processor 910. Main memory 915 canalso be used for storing position information, temporary variables, orother intermediate information during execution of instructions by theprocessor 910. The computing system 900 may further include a read onlymemory (ROM) 910 or other static storage device coupled to the bus 905for storing static information and instructions for the processor 910. Astorage device 925, such as a solid state device, magnetic disk oroptical disk, is coupled to the bus 905 for persistently storinginformation and instructions.

The computing system 900 may be coupled via the bus 905 to a display935, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 930, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 905 for communicating information and command selections to theprocessor 910. In another implementation, the input device 930 has atouch screen display 935. The input device 930 can include a cursorcontrol, such as a mouse, a trackball, or cursor direction keys, forcommunicating direction information and command selections to theprocessor 910 and for controlling cursor movement on the display 935.

According to various implementations, the processes described herein canbe implemented by the computing system 900 in response to the processor910 executing an arrangement of instructions contained in main memory915. Such instructions can be read into main memory 915 from anothercomputer-readable medium, such as the storage device 925. Execution ofthe arrangement of instructions contained in main memory 915 causes thecomputing system 900 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory915. In alternative implementations, hard-wired circuitry may be used inplace of or in combination with software instructions to effectillustrative implementations. Thus, implementations are not limited toany specific combination of hardware circuitry and software.

Implementations described in this specification can be implemented indigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.The implementations described in this specification can be implementedas one or more computer programs, i.e., one or more modules of computerprogram instructions, encoded on one or more computer storage media forexecution by, or to control the operation of, data processing apparatus.Alternatively or in addition, the program instructions can be encoded onan artificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate components or media (e.g., multiple CDs, disks, or otherstorage devices). Accordingly, the computer storage medium is bothtangible and non-transitory.

Other implementations are within the scope of the following claims. Insome cases, the actions recited in the claims can be performed in adifferent order and still achieve desirable results. In addition, theprocesses depicted in the accompanying figures do not necessarilyrequire the particular order shown, or sequential order, to achievedesirable results. In certain implementations, multitasking and parallelprocessing may be advantageous. Thus, particular implementations of theinvention have been described.

We claim:
 1. An apparatus comprising: a memory having computer readableinstructions stored thereon; and processing circuitry configured toexecute the computer readable instructions to cause the apparatus to,calculate a volatility score for each security of a first account basedon historical price values of the respective security over a desiredtime period, the first account including at least one security andassociated with a first user; select a benchmark index, the benchmarkindex including a set of securities related to a security type of thefirst account; calculate a reference volatility score for the set ofsecurities of the benchmark index based on historical price values ofthe set of securities over the desired time period; calculate a relativevolatility score for each security of the first account based on thevolatility score for each security of the first account and thereference volatility score; generate a graphical user interface based onthe calculated relative volatility score for each security of the firstaccount over the desired time period; and display the generatedgraphical user interface on a display screen corresponding to the firstuser.
 2. The apparatus of claim 1, wherein the apparatus is furthercaused to: calculate an aggregated volatility score for the firstaccount based on the relativity volatility scores for each security ofthe first account; calculate an aggregated volatility score for aplurality of second accounts over the desired time period based onrelative volatility scores associated with the securities held withinthe respective second account; and generate the graphical user interfacebased on the aggregated volatility score for the first account and theaggregated volatility score for the plurality of second accounts overthe desired time period.
 3. The apparatus of claim 2, wherein theapparatus is further caused to: determine security recommendations forthe first user based on the aggregated volatility score for the firstaccount and the aggregated volatility score for the plurality of secondaccounts; and display the determined security recommendations for thefirst user on the generated graphical user interface.
 4. The apparatusof claim 2, wherein the apparatus is further caused to: receive a userinput from the first user, the user input including a target aggregatevolatility score and a desired second time period; search the aggregatedvolatility scores for the plurality of second accounts for securityrecommendations based on the target aggregate volatility score and thedesired second time period; and display the security recommendations onthe generated graphical user interface.
 5. The apparatus of claim 4,wherein the apparatus is further caused to: calculate a differencebetween the target aggregate volatility score and the aggregatedvolatility score of the first account; determine a holding strategyassociated with the security recommendations based on the calculateddifference; and display the holding strategy to the first user on thegenerated graphical user interface.
 6. The apparatus of claim 1, whereinthe apparatus is further caused to: determine a trust factor between thefirst account and the benchmark index based on a correlation between anaggregated price movement of all of the securities of the first accountand an aggregated price movement of all of the securities of thebenchmark index during the desired time period; update the relativevolatility score for each security of the first account based on thedetermined trust factor; and display the updated relative volatilityscore for each security of the first account on the generated graphicaluser interface.
 7. The apparatus of claim 1, wherein the security typeof the first account is at least one of: a stock, a stock index, acustom list of stocks, real estate, a collectible, or any combinationsthereof.
 8. A method of operating an apparatus, comprising: calculatinga volatility score for each security of a first account based onhistorical price values of the respective security over a desired timeperiod, the first account including at least one security and associatedwith a first user; selecting a benchmark index, the benchmark indexincluding a set of securities associated with a security type of thefirst account; calculating a reference volatility score for the set ofsecurities of the benchmark index based on historical price values ofthe set of securities over the desired time period; calculating arelative volatility score for each security of the first account basedon the volatility score for each security of the first account and thereference volatility score; generating a graphical user interface basedon the calculated relative volatility score for each security of thefirst account over the desired time period; and displaying the generatedgraphical user interface on a display screen corresponding to the firstuser.
 9. The method of claim 8, further comprising: calculating anaggregated volatility score for the first account based on therelativity volatility scores for each security of the first account;calculating an aggregated volatility score for a plurality of secondaccounts over the desired time period based on relative volatilityscores associated with the securities held within the respective secondaccount; and generating the graphical user interface based on theaggregated volatility score for the first account and the aggregatedvolatility score for the plurality of second accounts over the desiredtime period.
 10. The method of claim 9, further comprising: determiningsecurity recommendations for the first user based on the aggregatedvolatility score for the first account and the aggregated volatilityscore for the plurality of second accounts; and displaying thedetermined security recommendations for the first user on the generatedgraphical user interface.
 11. The method of claim 9, further comprising:receiving a user input from the first user, the user input including atarget aggregate volatility score and a desired second time period;searching the aggregated volatility scores for the plurality of secondaccounts for security recommendations based on the target aggregatevolatility score and the desired second time period; and displaying thesecurity recommendations on the generated graphical user interface. 12.The method of claim 11, further comprising: calculating a differencebetween the target aggregate volatility score and the aggregatedvolatility score of the first account; determining a holding strategyassociated with the security recommendations based on the calculateddifference; and displaying the holding strategy to the first user on thegenerated graphical user interface.
 13. The method of claim 8, furthercomprising: determining a trust factor between the first account and thebenchmark index based on a correlation between an aggregated pricemovement of all of the securities of the first account and an aggregatedprice movement of all of the securities of the benchmark index duringthe desired time period; updating the relative volatility score for eachsecurity of the first account based on the determined trust factor; anddisplaying the updated relative volatility score for each security ofthe first account on the generated graphical user interface.
 14. Themethod of claim 8, wherein the security type of the first account is atleast one of: a stock, a stock index, a custom list of stocks, realestate, a collectible, or any combinations thereof.
 15. A non-transitorycomputer readable medium having computer readable instructions storedthereon, which when executed by processing circuitry, causes theprocessing circuitry to: calculate a volatility score for each securityof a first account based on historical price values of the respectivesecurity over a desired time period, the first account including atleast one security and associated with a first user; select a benchmarkindex, the benchmark index including a set of securities associated witha security type of the first account; calculate a reference volatilityscore for the set of securities of the benchmark index based onhistorical price values of the set of securities over the desired timeperiod; calculate a relative volatility score for each security of thefirst account based on the volatility score for each security of thefirst account and the reference volatility score; generate a graphicaluser interface based on the calculated relative volatility score foreach security of the first account over the desired time period; anddisplay the generated graphical user interface on a display screencorresponding to the first user.
 16. The non-transitory computerreadable medium of claim 15, wherein the processing circuitry is furthercaused to: calculate an aggregated volatility score for the firstaccount based on the relativity volatility scores for each security ofthe first account; calculate an aggregated volatility score for aplurality of second accounts over the desired time period based on basedon the relative volatility scores associated with the securities heldwithin the respective second account; and generate the graphical userinterface based on the aggregated volatility score for the first accountand the aggregated volatility score for the plurality of second accountsover the desired time period.
 17. The non-transitory computer readablemedium of claim 16, wherein the processing circuitry is further causedto: determine security recommendations for the first user based on theaggregated volatility score for the first account and the aggregatedvolatility score for the plurality of second accounts; and display thedetermined security recommendations for the first user on the generatedgraphical user interface.
 18. The non-transitory computer readablemedium of claim 16, wherein the processing circuitry is further causedto: receive a user input from the first user, the user input including atarget aggregate volatility score and a desired second time period;search the aggregated volatility scores for the plurality of secondaccounts for security recommendations based on the target aggregatevolatility score and the desired second time period; and display thesecurity recommendations on the generated graphical user interface. 19.The non-transitory computer readable medium of claim 18, wherein theprocessing circuitry is further caused to: calculate a differencebetween the target aggregate volatility score and the aggregatedvolatility score of the first account; determine a holding strategyassociated with the security recommendations based on the calculateddifference; and display the holding strategy to the first user on thegenerated graphical user interface.
 20. The non-transitory computerreadable medium of claim 15, wherein the processing circuitry is furthercaused to: determine a trust factor between the first account and thebenchmark index based on a correlation between an aggregated pricemovement of all of the securities of the first account and an aggregatedprice movement of all of the securities of the benchmark index duringthe desired time period; update the relative volatility score for eachsecurity of the first account based on the determined trust factor; anddisplay the updated relative volatility score for each security of thefirst account on the generated graphical user interface.